{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T15:39:21Z","timestamp":1785512361658,"version":"3.56.0"},"reference-count":39,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:00:00Z","timestamp":1689552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea Agency for Technology and Standards in 2022","award":["K_G012002236201, K_G012002234001"],"award-info":[{"award-number":["K_G012002236201, K_G012002234001"]}]},{"name":"Korea Agency for Technology and Standards in 2022","award":["GCU- 202300770001"],"award-info":[{"award-number":["GCU- 202300770001"]}]},{"name":"Gachon University research fund of 2022","award":["K_G012002236201, K_G012002234001"],"award-info":[{"award-number":["K_G012002236201, K_G012002234001"]}]},{"name":"Gachon University research fund of 2022","award":["GCU- 202300770001"],"award-info":[{"award-number":["GCU- 202300770001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Drowsy driving can significantly affect driving performance and overall road safety. Statistically, the main causes are decreased alertness and attention of the drivers. The combination of deep learning and computer-vision algorithm applications has been proven to be one of the most effective approaches for the detection of drowsiness. Robust and accurate drowsiness detection systems can be developed by leveraging deep learning to learn complex coordinate patterns using visual data. Deep learning algorithms have emerged as powerful techniques for drowsiness detection because of their ability to learn automatically from given inputs and feature extractions from raw data. Eye-blinking-based drowsiness detection was applied in this study, which utilized the analysis of eye-blink patterns. In this study, we used custom data for model training and experimental results were obtained for different candidates. The blinking of the eye and mouth region coordinates were obtained by applying landmarks. The rate of eye-blinking and changes in the shape of the mouth were analyzed using computer-vision techniques by measuring eye landmarks with real-time fluctuation representations. An experimental analysis was performed in real time and the results proved the existence of a correlation between yawning and closed eyes, classified as drowsy. The overall performance of the drowsiness detection model was 95.8% accuracy for drowsy-eye detection, 97% for open-eye detection, 0.84% for yawning detection, 0.98% for right-sided falling, and 100% for left-sided falling. Furthermore, the proposed method allowed a real-time eye rate analysis, where the threshold served as a separator of the eye into two classes, the \u201cOpen\u201d and \u201cClosed\u201d states.<\/jats:p>","DOI":"10.3390\/s23146459","type":"journal-article","created":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T01:46:02Z","timestamp":1689644762000},"page":"6459","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":97,"title":["Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6612-8176","authenticated-orcid":false,"given":"Furkat","family":"Safarov","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-si 461701, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5360-3479","authenticated-orcid":false,"given":"Farkhod","family":"Akhmedov","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-si 461701, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5923-8695","authenticated-orcid":false,"given":"Akmalbek Bobomirzaevich","family":"Abdusalomov","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-si 461701, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1988-5376","authenticated-orcid":false,"given":"Rashid","family":"Nasimov","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Tashkent State University of Economics, Tashkent 100066, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0184-7599","authenticated-orcid":false,"given":"Young Im","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-si 461701, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1007\/s10588-021-09345-w","article-title":"A multi-value cellular automata model for multi-lane traffic flow under lagrange coordinate","volume":"28","author":"Zeng","year":"2022","journal-title":"Comput. 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